This four-year K01 award application is to provide intensive multi-disciplinary training and career development guidance for Dr. Yu-Hsiang Hsieh, PhD, an Assistant Professor in Emergency Medicine at the Johns Hopkins University, leading to his becoming an independent investigator. Under the guidance of world renowned, NIH-funded, experienced mentors, Drs. Richard Rothman, Joshua Epstein, and David Holtgrave, Dr. Hsieh will gain a set of complementary inter-disciplinary skills which will allow him to pursue research with a focus on devising and evaluating public health interventions for infectious diseases seen in acute care settings. The applicant will receive mentoring in: public health aspects of emergency medicine; infectious disease computational mathematical modeling; and cost-effectiveness analysis. The rationale for the work, evaluation of HIV screening strategies, comes from the fact that HIV incidence has remained unchanged in the U.S. over the past decade, with one in five infected remaining unaware of their serostatus. One of the cornerstones recently advanced by the CDC for controlling the epidemic is widespread screening in emergency department (ED) settings. While, increasing numbers of EDs across the U.S. have developed strategies to adapt HIV testing into routine practice, there have been no efforts to systematically address how effective ED-based HIV testing has been (relative to no ED-based testing), or, which of several testing strategies is optimal with regard to identifying the largest number of unrecognized cases, and decreasing community HIV transmission. A relatively new and innovative method of computational modeling, agent-based modeling (ABM), offers a potential means for handling the complexity of temporal dynamics of HIV epidemiology and human behaviors associated with availability of ED-based HIV testing services, and represents a promising approach for overcoming many of the shortcoming of conventional study designs. The goal of this research project is to: 1) derive a computational model for measuring the impact of ED HIV testing programs on the HIV epidemic in Baltimore, Maryland, using ABM methods; 2) validate the derived ABM model; and 3) assess relative cost- effectiveness of ED HIV testing programs based on findings from the validated ABM model. The results of this study will provide new insights regarding current approaches to ED-based HIV testing programs and provide important cost-effectiveness data to inform local and national HIV testing strategies. The K01 award will provide Dr. Hsieh an intensive and directed mentored research experience in two new disciplines for him - infectious disease computational mathematical modeling, particularly ABM, and cost-effectiveness analysis. He will also receive needed training in the responsible conduct of research as well as longitudinal career guidance from an experience team. Together these experiences will equip him with the essential training and mentorship necessary to become an outstanding interdisciplinary independent public health investigator, who will build a research program focused on the study of infectious diseases in emergent, and acute care venues. PROJECT NARRATIVE: Emergency department-based HIV testing programs in the United States have identified thousands of previously undiagnosed HIV-infected individuals since 2006, when the Centers of Disease Control and Prevention began to aggressively promote testing in this non-traditional health care venue. The applicant will pursue a period of intensive multi-disciplinary training in infectious disease mathematical modeling - particularly agent based modeling, and cost-effectiveness analysis, applying these methods to study public health screening in acute care settings. The rich research environment of Johns Hopkins University coupled with the nationally recognized team of expert mentors will lead to independent investigator status, and a career dedicated to research in the development and evaluation of public health intervention strategies which address a wide array of infectious diseases' threats seen in emergency and other acute episodic care settings.